4 citations · 17 across the 25 of their papers we have counts for
Showing 2023 · stat.MLShow all
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stat.ML2023★ 1 cited
QuantEase: Optimization-based Quantization for Language Models
Kayhan Behdin, Ayan Acharya, Aman Gupta +4
With the rising popularity of Large Language Models (LLMs), there has been an increasing interest in compression techniques that enable their efficient deployment. This study focus…
stat.ML2023★ 1 cited
On Statistical Properties of Sharpness-Aware Minimization: Provable Guarantees
Kayhan Behdin, Rahul Mazumder
Sharpness-Aware Minimization (SAM) is a recent optimization framework aiming to improve the deep neural network generalization, through obtaining flatter (i.e. less sharp) solution…
stat.ML2023★ 1 cited
mSAM: Micro-Batch-Averaged Sharpness-Aware Minimization
Kayhan Behdin, Qingquan Song, Aman Gupta +7
Modern deep learning models are over-parameterized, where different optima can result in widely varying generalization performance. The Sharpness-Aware Minimization (SAM) technique…